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Ilya Tolstikhin
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Wasserstein auto-encoders
I Tolstikhin, O Bousquet, S Gelly, B Schoelkopf
arXiv preprint arXiv:1711.01558, 426-433, 2017
8112017
Mlp-mixer: An all-mlp architecture for vision
IO Tolstikhin, N Houlsby, A Kolesnikov, L Beyer, X Zhai, T Unterthiner, ...
Advances in Neural Information Processing Systems 34, 2021
3202021
Adagan: Boosting generative models
IO Tolstikhin, S Gelly, O Bousquet, CJ Simon-Gabriel, B Schölkopf
Advances in neural information processing systems 30, 2017
2192017
Towards a learning theory of cause-effect inference
D Lopez-Paz, K Muandet, B Schölkopf, I Tolstikhin
International Conference on Machine Learning, 1452-1461, 2015
1592015
From optimal transport to generative modeling: the VEGAN cookbook
O Bousquet, S Gelly, I Tolstikhin, CJ Simon-Gabriel, B Schoelkopf
URL http://arxiv. org/abs/1705.07642, 2017
1152017
Minimax estimation of kernel mean embeddings
I Tolstikhin, BK Sriperumbudur, K Muandet
The Journal of Machine Learning Research 18 (1), 3002-3048, 2017
532017
PAC-Bayes-empirical-Bernstein inequality
IO Tolstikhin, Y Seldin
Advances in Neural Information Processing Systems 26, 2013
522013
Minimax estimation of maximum mean discrepancy with radial kernels
IO Tolstikhin, BK Sriperumbudur, B Schölkopf
Advances in Neural Information Processing Systems 29, 2016
482016
On the latent space of wasserstein auto-encoders
PK Rubenstein, B Schoelkopf, I Tolstikhin
arXiv preprint arXiv:1802.03761, 2018
372018
What do neural networks learn when trained with random labels?
H Maennel, IM Alabdulmohsin, IO Tolstikhin, R Baldock, O Bousquet, ...
Advances in Neural Information Processing Systems 33, 19693-19704, 2020
312020
Differentially private database release via kernel mean embeddings
M Balog, I Tolstikhin, B Schölkopf
International Conference on Machine Learning, 414-422, 2018
292018
Practical and consistent estimation of f-divergences
P Rubenstein, O Bousquet, J Djolonga, C Riquelme, IO Tolstikhin
Advances in Neural Information Processing Systems 32, 2019
252019
Predicting neural network accuracy from weights
T Unterthiner, D Keysers, S Gelly, O Bousquet, I Tolstikhin
arXiv preprint arXiv:2002.11448, 2020
232020
Learning disentangled representations with wasserstein auto-encoders
PK Rubenstein, B Schölkopf, I Tolstikhin
182018
Localized complexities for transductive learning
I Tolstikhin, G Blanchard, M Kloft
Conference on Learning Theory, 857-884, 2014
162014
When can unlabeled data improve the learning rate?
C Göpfert, S Ben-David, O Bousquet, S Gelly, I Tolstikhin, R Urner
Conference on Learning Theory, 1500-1518, 2019
152019
Genet: Deep representations for metagenomics
M Rojas-Carulla, I Tolstikhin, G Luque, N Youngblut, R Ley, B Schölkopf
arXiv preprint arXiv:1901.11015, 2019
152019
Competitive training of mixtures of independent deep generative models
F Locatello, D Vincent, I Tolstikhin, G Rätsch, S Gelly, B Schölkopf
arXiv preprint arXiv:1804.11130, 2018
142018
Permutational rademacher complexity
I Tolstikhin, N Zhivotovskiy, G Blanchard
International Conference on Algorithmic Learning Theory, 209-223, 2015
122015
Probabilistic active learning of functions in structural causal models
PK Rubenstein, I Tolstikhin, P Hennig, B Schölkopf
arXiv preprint arXiv:1706.10234, 2017
102017
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Articles 1–20